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Proceedings Paper

A 3D model retrieve method integrating shape distribution and self-organizing feature map
Author(s): Meifa Huang; Hui Jing; Yanru Zhong; Bing Kuang
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Paper Abstract

Shape Distribution is fast, simple, and robust method in 3D model retrieve. This method, however, only considers distances between the objects' shape distribution histograms and ignores the information included. As the result, the retrieval precision is low. To enhance the retrieve efficiency, a novel method which integrates Shape Distribution and Self-Organizing Feature Map (SOFM) is proposed. The models' shape distribution histograms are established by Shape Distribution and transformed into the proper format of SOFM. The similar models are grouped in neighboring neurons of SOFM by using competitive learning approach. In addition, the dissimilar models are indexed in far away neurons. With the given query model, SOFM classifies it into the proper cluster and exports the retrieval results. A case study is presented and the results show that the retrieval precision of the proposed method is higher than that of the Shape Distribution method.

Paper Details

Date Published: 28 November 2007
PDF: 8 pages
Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 683309 (28 November 2007); doi: 10.1117/12.755925
Show Author Affiliations
Meifa Huang, Guilin Univ. of Electronic Technology (China)
Hui Jing, Guilin Univ. of Electronic Technology (China)
Yanru Zhong, Guilin Univ. of Electronic Technology (China)
Bing Kuang, Guilin Univ. of Electronic Technology (China)

Published in SPIE Proceedings Vol. 6833:
Electronic Imaging and Multimedia Technology V
Liwei Zhou; Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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